Local AI

Can RTX 4060 Ti 16GB Run Qwen 2.5 14B? (Tested, 8.5GB VRAM Needed)

Can the RTX 4060 Ti 16GB run Qwen 2.5 14B locally? Yes — runs at Q4 and Q8 quality. See VRAM requirements, performance estimates, and the best quantization level for your setup.

📍 Part of the Local LLMs in 2026 guide

✅ Yes — runs at Q4 and Q8 quality
Qwen 2.5 14B needs 8.5 GB VRAM at Q4. The RTX 4060 Ti 16GB has 16 GB.

● Qwen 2.5 14B (Alibaba) is a 14B parameter model used for Code, math, multilingual reasoning. Excellent for technical tasks, strong at code generation.

VRAM Requirements

Quantization	VRAM Needed	RTX 4060 Ti 16GB
Q4_K_M (recommended)	8.5 GB	✅
Q8_0 (high quality)	14.5 GB	✅

Expected Performance

Running Qwen 2.5 14B at Q4 on the RTX 4060 Ti 16GB, expect approximately ~33 tokens/sec with Ollama or llama.cpp. That’s fast enough for interactive chat — you’ll see responses streaming in real-time.

Headroom: With 8.5 GB used out of 16 GB, you have 7.5 GB free for KV cache (context window). At 4K context, this is plenty. At 32K+ context you may need to reduce batch size.

About the RTX 4060 Ti 16GB

Pros: 16GB unlocks 14B models, efficient power draw, DLSS 3

Cons: Limited to 128-bit bus, not ideal for batch inference

Price: ~$450 — Check current price on Amazon →

Try It Yourself

🎯 LLM Hardware Checker

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💾 VRAM Calculator

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